Multi-agent supply chain scheduling problem by considering resource allocation and transportation

Aminzadegan, Sajede; Tamannaei, Mohammad; Rasti−Barzoki, Morteza

doi:10.1016/j.cie.2019.106003

Cited by 25 publications

(9 citation statements)

References 41 publications

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“…Studies that fall under the Make category are those that integrate planning into production. Here researchers explored how supply chain scheduling can autonomously be linked to production scheduling (Wong et al, 2006;Aminzadegan, Tamannaei and Rasti-Barzoki, 2019), and how production orders can be automatically distributed among the supply chain (Reis, Mamede and O'Neill, 2001;Lima, Sousa and Martins, 2006;Gharaei and Jolai, 2018).…”

Section: Automation Across Supply Chain Functionsmentioning

confidence: 99%

Will bots take over the supply chain? Revisiting agent-based supply chain automation

Mak

Brintrup

2021

International Journal of Production Economics

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Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chain Management (SCM). Although agent-based supply chains systems have been proposed since the early 2000s; industrial uptake of them has been lagging. The reasons quoted include the immaturity of technology, a lack of interoperability with supply chain information systems, and a lack of trust in Artificial Intelligence (AI). In this paper, we revisit the agent-based supply chain and review the state of the art. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains; are filling in gaps, leaving the concept applicable to a wider range of functions. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation. Digital ledgers help securely transfer data between third parties, making agent-based information sharing possible, without the need to integrate Enterprise Resource Planning (ERP) systems. Learning functionality in agents enables agents to move beyond automation and towards autonomy. We note this convergence effect through conceptualising an agent-based supply chain framework, reviewing its components, and highlighting research challenges that need to be addressed in moving forward.

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Section: Automation Across Supply Chain Functionsmentioning

confidence: 99%

Will bots take over the supply chain? Revisiting agent-based supply chain automation

Mak

Brintrup

2021

International Journal of Production Economics

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“…Although the research on scheduling with multiple competing agents and scheduling with resource dependent processing times have attracted increasing attention recently, research on scheduling with resource dependent processing times in the multi‐agent setting is relatively scarce. As far as we know, the only works in this line are Aminzadegan, Tamannaei, and Rasti‐Barzoki (2019) and Wan, Vakati, Leung, and Pinedo (2010). Specifically, Aminzadegan et al (2019) consider two‐agent integrated scheduling involving production, transportation, and resource allocation to minimize the sum of the batch delivery cost, resource allocation cost, tardiness penalty cost, and total number of tardy orders.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As far as we know, the only works in this line are Aminzadegan, Tamannaei, and Rasti‐Barzoki (2019) and Wan, Vakati, Leung, and Pinedo (2010). Specifically, Aminzadegan et al (2019) consider two‐agent integrated scheduling involving production, transportation, and resource allocation to minimize the sum of the batch delivery cost, resource allocation cost, tardiness penalty cost, and total number of tardy orders. The authors provide two meta‐heuristic algorithms to solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The authors provide

NP

‐hardness proof for the general problem and polynomial‐time algorithms for several special cases of the problem. Compared with our problem, Aminzadegan et al (2019) seek to minimize an integrated optimality criterion, while Wan et al (2010) consider the case where only the jobs of one agent can be compressed and all the compressed jobs share a common compression rate. As for the solution methodologies, Aminzadegan et al (2019) develop meta‐heuristic algorithms, while Wan et al (2010) analyze the computational complexity of the general problem and develop polynomial‐time algorithms to solve special cases of the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Compared with our problem, Aminzadegan et al (2019) seek to minimize an integrated optimality criterion, while Wan et al (2010) consider the case where only the jobs of one agent can be compressed and all the compressed jobs share a common compression rate. As for the solution methodologies, Aminzadegan et al (2019) develop meta‐heuristic algorithms, while Wan et al (2010) analyze the computational complexity of the general problem and develop polynomial‐time algorithms to solve special cases of the problem. In contrast, we analyze the computational complexity of the problem, investigate the existence of polynomial‐time (1 + ϵ )‐approximation algorithms with ϵ < + ∞ and FPTAS, and devise a proximal bundle algorithm, which is a well‐known powerful bounding technique to derive sharp lower bounds and/or high‐quality feasible solutions for

NP

‐hard combinatorial optimization problems.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Two‐agent scheduling with linear resource‐dependent processing times

Wang

Qiu

et al. 2020

Naval Research Logistics

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This paper considers a two‐agent scheduling problem with linear resource‐dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is NP‐hard in the ordinary sense, and there does not exist a polynomial‐time approximation algorithm with performance ratio unless P=NP; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.

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